In reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems, the large-scale RIS introduces pronounced geometric effects that lead to the coexistence of far-field and near-field propagation. Furthermore, random blockages induce spatial non-stationarity across the RIS array, causing signals from different scatterers to illuminate only partial regions, referred to as visible regions (VRs). This renders conventional far-field and fully visible array-based channel models inadequate and makes channel estimation particularly challenging. In this paper, we investigate the non-stationary cascaded channel estimation problem in a hybrid-field propagation environment, where the RIS-base station (BS) link operates in the far-field, while the user-RIS link exhibits near-field characteristics with partial visibility. To address the resulting high-dimensional and coupled estimation problem, a reduced-dimensional sparse bilinear representation is developed by exploiting the structural characteristics of the cascaded channel. In particular, a dictionary compression technique is proposed to represent the high-dimensional coupled dictionary using a low-dimensional polar-domain dictionary weighted by a visibility matrix, thereby significantly reducing the problem scale. Based on this representation, a turbo-structured joint Bayesian estimation (TS-JBE) approach is proposed to simultaneously estimate the channel gains, VRs, and off-grid parameters, thereby avoiding error propagation inherent in existing sequential methods. Simulation results demonstrate that the proposed method significantly improves the estimation accuracy compared with existing approaches.

翻译：在可重构智能表面（RIS）辅助的毫米波通信系统中，大规模RIS引入显著的几何效应，导致远场与近场传播并存。此外，随机遮挡引起RIS阵列的空间非平稳性，使得来自不同散射体的信号仅照射部分区域，即所谓可见区域（VRs）。这使得传统的基于远场和全可见阵列的信道模型不再适用，并使信道估计尤为困难。本文研究混合场传播环境下的非平稳级联信道估计问题，其中RIS-基站（BS）链路工作在远场，而用户-RIS链路呈现具有部分可见性的近场特性。为解决由此产生的高维耦合估计问题，本文通过挖掘级联信道的结构特征，提出了一种降维稀疏双线性表示。具体而言，设计了一种字典压缩技术，利用由可见性矩阵加权的低维极坐标域字典来表示高维耦合字典，从而显著降低问题规模。基于此表示，提出了一种涡轮结构的联合贝叶斯估计（TS-JBE）方法，以同时估计信道增益、可见区域及离网参数，从而避免现有顺序方法中固有的误差传播。仿真结果表明，与现有方法相比，所提方法显著提高了估计精度。